| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: microsoft/resnet-50 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: resnet-50_rice-leaf-disease-augmented-v2_tl |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # resnet-50_rice-leaf-disease-augmented-v2_tl |
| |
|
| | This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.3083 |
| | - Accuracy: 0.5952 |
| |
|
| | ## Model description |
| |
|
| | More information needed |
| |
|
| | ## Intended uses & limitations |
| |
|
| | More information needed |
| |
|
| | ## Training and evaluation data |
| |
|
| | More information needed |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 0.0003 |
| | - train_batch_size: 128 |
| | - eval_batch_size: 128 |
| | - seed: 42 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: cosine_with_restarts |
| | - lr_scheduler_warmup_ratio: 0.1 |
| | - num_epochs: 20 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | | 2.0633 | 1.0 | 63 | 2.0143 | 0.3452 | |
| | | 1.9625 | 2.0 | 126 | 1.8719 | 0.5060 | |
| | | 1.8119 | 3.0 | 189 | 1.7332 | 0.5 | |
| | | 1.6826 | 4.0 | 252 | 1.6271 | 0.5268 | |
| | | 1.5879 | 5.0 | 315 | 1.5436 | 0.5595 | |
| | | 1.516 | 6.0 | 378 | 1.4871 | 0.5536 | |
| | | 1.4572 | 7.0 | 441 | 1.4566 | 0.5655 | |
| | | 1.4104 | 8.0 | 504 | 1.4224 | 0.5685 | |
| | | 1.3734 | 9.0 | 567 | 1.4033 | 0.5685 | |
| | | 1.3414 | 10.0 | 630 | 1.3735 | 0.5952 | |
| | | 1.3186 | 11.0 | 693 | 1.3579 | 0.5714 | |
| | | 1.2972 | 12.0 | 756 | 1.3402 | 0.5923 | |
| | | 1.2862 | 13.0 | 819 | 1.3342 | 0.5893 | |
| | | 1.2716 | 14.0 | 882 | 1.3271 | 0.5863 | |
| | | 1.2632 | 15.0 | 945 | 1.3210 | 0.6042 | |
| | | 1.2546 | 16.0 | 1008 | 1.3146 | 0.5923 | |
| | | 1.2485 | 17.0 | 1071 | 1.3061 | 0.6012 | |
| | | 1.25 | 18.0 | 1134 | 1.3090 | 0.5923 | |
| | | 1.2457 | 19.0 | 1197 | 1.3106 | 0.6042 | |
| | | 1.2466 | 20.0 | 1260 | 1.3083 | 0.5952 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.48.3 |
| | - Pytorch 2.5.1+cu124 |
| | - Datasets 3.3.2 |
| | - Tokenizers 0.21.0 |
| | |
